class_docstr: Loss for single shot multi-box object detection
  
  Args:
      opts: command-line arguments
class_docstr_tok: ["Loss","for","single","shot","multi-box","object","detection","Args",":","opts",":","command-line","arguments"]
docstr: Reset the unscaled coefficients for confidence and regression losses to small values
docstr_tok: ["Reset","the","unscaled","coefficients","for","confidence","and","regression","losses","to","small","values"]
code: def reset_unscaled_loss_values(self) -> None:
      """Reset the unscaled coefficients for confidence and regression losses to small values"""
      # initialize with very small float values
      self.unscaled_conf_loss = 1e-7
      self.unscaled_reg_loss = 1e-7
  
file_path: ml-cvnets/loss_fn/detection/ssd_multibox_loss.py
imports: import argparse
  from typing import Any, Dict
  import torch
  from torch import Tensor
  from torch.nn import functional
  from cvnets.misc.third_party.ssd_utils import hard_negative_mining
  from loss_fn import LOSS_REGISTRY
  from loss_fn.detection.base_detection_criteria import BaseDetectionCriteria
  from utils import logger
  from utils.ddp_utils import is_master
  from utils.tensor_utils import tensor_to_python_float
label_desc: Use image node_id 2 for calling the SSDLoss obj's underlying member method code with example usage: obj.reset_unscaled_loss_values() without return types